Abstract Arterial-venous classification of retinal blood vessels is important for the automatic detection of cardiovascular diseases such as hypertensive retinopathy and stroke. In this paper, we propose an arterial-venous classification (AVC) method, which focuses on feature extraction and selection from vessel centerline pixels. The vessel centerline is extracted after the preprocessing of vessel segmentation and optic disc (OD) localization. Then, a region of interest (ROI) is extracted around OD, and the most efficient features of each centerline pixel in ROI are selected from the local features, grey-level co-occurrence matrix (GLCM) features, and an adaptive local binary patten (A-LBP) feature by using a max-relevance and min-redundancy (mRMR) scheme. Finally, a feature-weighted K-nearest neighbor (FW-KNN) algorithm is used to classify the arterial-venous vessels. The experimental results on the DRIVE database and INSPIRE-AVR database achieve the high accuracy of 88.65% and 88.51% in ROI, respectively.

This work was supported by the National Natural Science Foundation of China under Grant Nos. 61573380, 61702559, 61562029.

Corresponding Authors: Cheng-Zhang Zhu
Email: anandawork@126.com

About author: Bei-Ji Zou received his B.S.degree in computer software from Zhejiang University,Hangzhou,in 1982,his M.S.and Ph.D.degrees in computer science and technology from Tsinghua University,Beijing,in 1984,and Hunan University,Changsha,in 2001,respectively.He joined the School of Computer and Communication at Hunan University,Changsha,in 1984,where he became an associate professor in 1997,and a professor in 2001.